Predicting Effectiveness of Automatic Testing Tools

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Predicting Effectiveness of Automatic Testing Tools

Brett Daniel and Marat Boshernitsan
In ASE 2008: 23rd IEEE/ACM International Conference on Automated Software Engineering
L'Aquila, Italy. September, 2008


Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand.

We develop a technique that uses structural program metrics to predict the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision tree classifiers. Our experiments show that these classifiers can predict high or low coverage with success rates of 82% to 94%.

Tool Links

List of Metrics

Experimental Data